MR-Adopt: Automatic Deduction of Input Transformation Function for Metamorphic Testing

[2408.15815] MR-Adopt: Automatic Deduction of Input Transformation Function for Metamorphic Testing Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2408.15815 (cs) [Submitted on 28 Aug 2024 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: MR-Adopt: Automatic Deduction of Input Transformation Function for Metamorphic Testing Authors: Congying Xu , Songqiang Chen , Jiarong Wu , Shing-Chi Cheung , Valerio Terragni , Hengcheng Zhu , Jialun Cao View a PDF of the paper titled MR-Adopt: Automatic Deduction of Input Transformation Function for Metamorphic Testing, by Congying Xu and 6 other authors View PDF HTML (experimental) Abstract: While a recent study reveals that many developer-written test cases can encode a reusable Metamorphic Relation (MR), over 70% of them directly hard-code the source input and follow-up input in the encoded relation. Such encoded MRs, which do not contain an explicit input transformation to transform the source inputs to corresponding follow-up inputs, cannot be reused with new source inputs to enhance test adequacy. In this paper, we propose MR-Adopt (Automatic Deduction Of inPut Transformation) to automatically deduce the input transformation from the hard-coded source and follow-up inputs, aiming to enable the encoded MRs to be reused with new source inputs. With typically only one pair of source and follow-up inputs available in an MR-encoded test case as the example, we leveraged LLMs to understand the intention of the test case and generate additional examples of source-followup input pairs. This helps to guide the generation of input transformations generalizable to multiple source inputs. Besides, to mitigate the issue that LLMs generate erroneous code, we refine LLM-generated transformations by removing MR- irrelevant code elements with data-flow analysis. Finally, we assess candidate transformations based on encoded output relations and select the best transformation as the result. Evaluation results show that MR-Adopt can generate input transformations applicable to all experimental source inputs for 72.00% of encoded MRs, which is 33.33% more than using vanilla GPT-3.5. By incorporating MR- Adopt-generated input transformations, encoded MR-based test cases can effectively enhance the test adequacy, increasing the line coverage and mutation score by 10.62% and 18.91%, respectively. Comments: NOTE: Accepted to ASE 2024 Subjects: Software Engineering (cs.SE) Cite as: arXiv:2408.15815 [cs.SE] (or arXiv:2408.15815v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2408.15815 Focus to learn more arXiv-issued DOI via DataCite Journal reference: In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024), 557-569 Related DOI : https://doi.org/10.1145/3691620.3696020 Focus to learn more DOI(s) linking to related resources Submission history From: Congying Xu [ view email ] [v1] Wed, 28 Aug 2024 14:24:48 UTC (11,582 KB) [v2] Sat, 11 Apr 2026 09:31:33 UTC (11,582 KB) Full-text links: Access Paper: View a PDF of the paper titled MR-Adopt: Automatic Deduction of Input Transformation Function for Metamorphic Testing, by Congying Xu and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2024-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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